Endocrinol Metab (Seoul). 2022;37(2):369-382.

Outcome-Based Decision-Making Algorithm for Treating Patients with Primary Aldosteronism.

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제1저자 김정희 · 교신저자 이승훈

PMID 35417953 PMC9081309 DOI 10.3803/EnM.2022.1391 IF 5.1 내분비대사내과

소속 (8)

Department of Internal Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
Department of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul;, Korea.
Department of Radiology, Chung-Ang University Health Care System Hyundae Hospital, Namyangju, Korea.
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, Korea.
Division of Endocrinology and Metabolism, Department of Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Abstract

BACKGROUND Optimal management of primary aldosteronism (PA) is crucial due to the increased risk of cardiovascular and cerebrovascular diseases. Adrenal venous sampling (AVS) is the gold standard method for determining subtype but is technically challenging and invasive. Some PA patients do not benefit clinically from surgery. We sought to develop an algorithm to improve decision- making before engaging in AVS and surgery in clinical practice.

METHODS We conducted the ongoing Korean Primary Aldosteronism Study at two tertiary centers. Study A involved PA patients with successful catheterization and a unilateral nodule on computed tomography and aimed to predict unilateral aldosterone-producing adenoma (n=367). Study B involved similar patients who underwent adrenalectomy and aimed to predict postoperative outcome (n=330). In study A, we implemented important feature selection using the least absolute shrinkage and selection operator regression.

RESULTS We developed a unilateral PA prediction model using logistic regression analysis: lowest serum potassium level ≤3.4 mEq/L, aldosterone-to-renin ratio ≥150, plasma aldosterone concentration ≥30 ng/mL, and body mass index <25 kg/m2 (area under the curve, 0.819; 95% confidence interval, 0.774 to 0.865; sensitivity, 97.6%; specificity, 25.5%). In study B, we identified female, hypertension duration <5 years, anti-hypertension medication <2.5 daily defined dose, and the absence of coronary artery disease as predictors of clinical success, using stepwise logistic regression models (sensitivity, 94.2%; specificity, 49.3%). We validated our algorithm in the independent validation dataset (n=53).

CONCLUSION We propose this new outcome-driven diagnostic algorithm, simultaneously considering unilateral aldosterone excess and clinical surgical benefits in PA patients.

Keywords

AdrenalectomyClinical decision rulesHyperaldosteronismPatient selectionTreatment outcome

Figures

**Fig. 1.** Flow diagram of study subjects. PA, primary aldosteronism; CT, computed tomography; AVS, adrenal venous sampling.

**Fig. 2.** Variable selection using the least absolute shrinkage and selection operator (LASSO) regression model in the training set to predict concordance between computed tomography imaging and adrenal venous sampling findings (n==257). Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via 1 standard error estimates. The final values used for the model were cost=0.5, and epsilon=0.001. Accuracy was used to select the optimal model using the largest value. (A) Plot of the importance coefficient for each variable in the LASSO model. (B) Confusion matrix showing real and predicted clinical outcomes, the accuracy, sensitivity, specificity, and 10-fold cross-validation of the LASSO model. (C) Area under the curve (AUC) and cutoff point of four selected variables using the R package “OptimalCutpoints.” ARR, aldosterone-to-renin ratio; PAC, plasma aldosterone concentration; PRA, plasma renin activity; BMI, body mass index; HTN, hypertension; DDD, daily defined dose; DBP, diastolic blood pressure; SBP, systolic blood pressure; CVD, cerebrovascular disease; eGFR, estimated glomerular filtration rate; DM, diabetes mellitus; OSA, obstructive sleep apnea; CAD, coronary artery disease.

**Fig. 3.** The discrimination ability of Primary Aldosteronism Predicting Subtype (PAPS) score to detection of concordance between computed tomography imaging and adrenal venous sampling findings. (A) Receiver operating characteristic (ROC) curve for assessing the area under the curve (AUC) and the best cutoff point for the maximal sensitivity for unilateral excess in the training set. (B) Confusion matrix showing real and predicted clinical outcomes, the accuracy, sensitivity, and specificity for the training, test, and combined sets. CI, confidence interval.

**Fig. 4.** Clinical decision-making algorithm for primary aldosteronism (PA) patients with unilateral adrenal nodules on computed tomography (CT). PAPS, Primary Aldosteronism Predicting Subtype; ARR, aldosterone-to-renin ratio; PAC, plasma aldosterone concentration; BMI, body mass index; AVS, adrenal venous sampling; PAPSO, Primary Aldosteronism Predicting Surgical Outcome score; HTN, hypertension; DDD, daily defined dose; CAD, coronary artery disease.

그림 출처: PubMed Central 공개 논문 (PMC9081309)

Conflict of interest statement

CONFLICTS OF INTEREST . No potential conflict of interest relevant to this article was reported.

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